Travel time forecasting on a freeway corridor: a dynamic information fusion model based on the random forests approach

Abstract

Purpose Metropolitan areas suffer from frequent road traffic congestion not only during peak hours but also during off-peak periods. Different machine learning methods have been used in travel time prediction, however, such machine learning methods practically face the problem of overfitting. Tree-based ensembles have been applied in various prediction fields, and such approaches usually produce high prediction accuracy by aggregating and averaging individual decision trees. The inherent advantages of these approaches not only get better prediction results but also have a good bias-variance trade-off which can help to avoid overfitting. However, the reality is that the application of tree-based integration algorithms in traffic prediction is still limited. This study aims to improve the accuracy and interpretability of the models by using random forest (RF) to analyze and model the travel time on freeways. Design/methodology/approach As the traffic conditions often greatly change, the prediction results are often unsatisfactory. To improve the accuracy of short-term travel time prediction in the freeway network, a practically feasible and computationally efficient RF prediction method for real-world freeways by using probe traffic data was generated. In addition, the variables’ relative importance was ranked, which provides an investigation platform to gain a better understanding of how different contributing factors might affect travel time on freeways. Findings The parameters of the RF model were estimated by using the training sample set. After the parameter tuning process was completed, the proposed RF model was developed. The features’ relative importance showed that the variables (travel time 15 min before) and time of day (TOD) contribute the most to the predicted travel time result. The model performance was also evaluated and compared against the extreme gradient boosting method and the results indicated that the RF always produces more accurate travel time predictions. Originality/value This research developed an RF method to predict the freeway travel time by using the probe vehicle-based traffic data and weather data. Detailed information about the input variables and data pre-processing were presented. To measure the effectiveness of proposed travel time prediction algorithms, the mean absolute percentage errors were computed for different observation segments combined with different prediction horizons ranging from 15 to 60 min.

Predicting Traffic Congestion During Covid19 Using Human Mobility and Street-Waste Features

“…Most of these methods assume linearity and stationary behaviour, which encourages researchers to use ML models to deal with traffic data that are nonlinear, dynamic, and contain spatiotemporal dependencies. Supervised ML approaches such as Support Vector Machine (SVM) (Zhang et al, 2018;Bratsas et al, 2020), Random Forest (Qiu and Fan, 2021;Bratsas et al, 2020), and Gradient Boost algorithms (Toan and Truong, 2021) are representative models used in urban traffic studies. In the following, we review state of the art and categorize the literature on traffic congestion before and after the COVID-19 outbreak.…”

“…By contrast, ML can handle vast amounts of data, has a flexible modelling capability, can generalize and adapt, and are generally good at prediction tasks (Karlaftis and Vlahogianni, 2011). In this study, we used four widely used ML models in traffic s tudies ( Qiu and Fan, 2021;Zhang et al, 2018;Bratsas et al, 2020): Multiple Linear Regression (MLR), Random Forest (RF), Light Gradient Boosting (LGBM), and Support Vector Machine (SVR) to predict traffic congestion in Dublin city during COVID-19.…”